A multivariable model of ultrasound and clinicopathological features for predicting axillary nodal burden of breast cancer: potential to prevent unnecessary axillary lymph node dissection

Abstract Background To develop a clinical model for predicting high axillary nodal burden in patients with early breast cancer by integrating ultrasound (US) and clinicopathological features. Methods and materials Patients with breast cancer who underwent preoperative US examination and breast surgery at the Affiliated Hospital of Nantong University (centre 1, n = 250) and at the Affiliated Hospital of Jiangsu University (centre 2, n = 97) between January 2012 and December 2016 and between January 2020 and March 2022, respectively, were deemed eligible for this study (n = 347). According to the number of lymph node (LN) metastasis based on pathology, patients were divided into two groups: limited nodal burden (0–2 metastatic LNs) and heavy nodal burden (≥ 3 metastatic LNs). In addition, US features combined with clinicopathological variables were compared between these two groups. Univariate and multivariate logistic regression analysis were conducted to identify the most valuable variables for predicting ≥ 3 LNs in breast cancer. A nomogram was then developed based on these independent factors. Results Univariate logistic regression analysis revealed that the cortical thickness (p